The present invention relates to enhancement of the quality of an image, and more particularly, to an image enhancement circuit and a method for image enhancement using quantized mean-matching histogram equalization.
The basic operation of histogram equalization is to convert an input image on the basis of the histogram of the input image, wherein a histogram denotes the gray level distribution of an input image.
The histogram of a gray level provides an overall depiction of the appearance of an image. A gray level properly controlled according to a sample distribution of an image improves the appearance or contrast of an image.
Histogram equalization, as a method for enhancing the contrast of a given image according to a sample distribution of the image, is the most widely known of the various methods for contrast enhancement, and is disclosed in the following documents: 1! J. S. Lim, "Two-Dimensional Signal and Image Processing, Prentice Hall," Englewood Cliffs, N.J., 1990; and 2! R. C. Gonzalez and P. Wints, "Digital Image Processing," Addison-Wesley, Reading, Mass., 1977.
Also, a useful application of a histogram equalization method in the fields of medical image processing and radar image processing is disclosed in the following documents: 3! J. Zimmerman, S. Pizer, E. Staab, E. Perry, W. McCartney, and B. Brenton, "Evaluation of the Effectiveness of Adaptive Histogram Equalization for Contrast Enhancement," IEEE Tr. on Medical Imaging, pp. 304-312, December 1988; and 4! Y. Li, W. Wang, and D. Y. Yu, "Application of Adaptive Histogram Equalization to X-ray Chest Image," Proc. of the SPIE, pp. 513-514, vol. 2321, 1994.
Accordingly, a technique using a histogram of a given image has been usefully applied to various fields such as medical image processing, infrared image processing, radar image processing, etc.
In general, since histogram equalization has an effect of stretching a dynamic range, it can flatten the distribution density of a resultant image, thereby enhancing the contrast of an image. However, such characteristics of histogram equalization can become a disadvantage in practical applications. That is, since the output density of histogram equalization is constant, the mean brightness of an output image approaches a middle gray level. In practice, in order to accomplish histogram equalization for an analog image, the mean brightness of an output image in histogram equalization is an exact middle gray level regardless of the mean brightness of an input image. Obviously, this characteristic is not desirable for practical applications. For instance, a problem occurs in that a scene photographed at night appears extremely bright after histogram equalization.
Furthermore, a conventional histogram equalization circuit needs a configuration for storing generated frequencies of every gray level, which increases hardware costs. For example, if a gray level (L) is 256, 256 memory devices are required to store the generated frequencies of every gray level, and 256 accumulators are required to accumulate the generated frequencies of every gray level.